首页|Data on Machine Learning Published by a Researcher at Shenyang Agricultural Univ ersity (Early Detection of Rice Leaf Blast Disease Using Unmanned Aerial Vehicle Remote Sensing: A Novel Approach Integrating a New Spectral Vegetation Index an d ...)
Data on Machine Learning Published by a Researcher at Shenyang Agricultural Univ ersity (Early Detection of Rice Leaf Blast Disease Using Unmanned Aerial Vehicle Remote Sensing: A Novel Approach Integrating a New Spectral Vegetation Index an d ...)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on artificial intelligen ce have been presented. According to news reporting from Shenyang, People's Repu blic of China, by NewsRx journalists, research stated, "Leaf blast is recognized as one of the most devastating diseases affecting rice production in the world, seriously threatening rice yield." Financial supporters for this research include Liaoning Province Applied Basic R esearch Program Project; National Natural Science Foundation of China Youth Prog ram. Our news journalists obtained a quote from the research from Shenyang Agricultur al University: "Therefore, early detection of leaf blast is extremely important to limit the spread and propagation of the disease. In this study, a leaf blast- specific spectral vegetation index RBVI = 9.78R816-R724-2.08(r736/R724) was de signed to qualitatively detect the level of leaf blast disease in the canopy of a field and to improve the accuracy of early detection of leaf blast by remote s ensing by unmanned aerial vehicle. Stacking integrated learning, AdaBoost, and S VM were used to compare and analyze the performance of the RBVI and traditional vegetation index for early detection of leaf blast. The results showed that the stacking model constructed based on the RBVI spectral index had the highest dete ction accuracy (OA: 95.9%, Kappa: 93.8%). Compared to stacking, the detection accuracy of the SVM and AdaBoost models constructed base d on the RBVI is slightly degraded. Compared with conventional SVIs, the RBVI ha d higher accuracy in its ability to qualitatively detect leaf blast in the field ."
Shenyang Agricultural UniversityShenya ngPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Le arningRemote Sensing
NETL
NSTL
